Unscented Trainable Kalman Filter Based on Deep Learning Method Considering Incomplete Information

Abstract

Rapid changes of states and occurrence of data missing in power systems cause accurate state estimation very hard. In this paper, an unscented trainable Kalman filter (UTKF) with a deep learning prediction model is proposed to provide accurate state estimation under incomplete information. First, the CNN-LSTM architecture, a typical deep learning model, is applied to form a trainable prediction model (TPM), which offers more accurate prediction of states. However, sometimes states are incomplete due to data losses in transmissions. To deal with incomplete information, historical time-series states are employed and fed to the TPM in order to develop a missing data filling method. In this way, the prediction errors can be lower through the online training and parameter adjustment of the TPM. Combining with the TPM and the missing data filling method, an unscented trainable Kalman filter (UTKF) is proposed to improve the state estimation of power systems when incomplete information is involved. Finally, three cases are designed, and the simulation results show that for the prediction of states, the root mean square error (RMSE), an indicator of accuracies, is reduced by about 3 multiples, if our missing data filling method is added. Furthermore, the accuracy of state estimation is improved about 5 multiples by the proposed UTKF method, even if incomplete information is involved.